AI Governance for Property and Casualty Claims Triage

Why Claims Triage Requires Governance Beyond Model Accuracy

AI agents used for claims triage are typically evaluated on how well they classify severity, route claims, or flag potential fraud indicators. That evaluation misses the more consequential risk. A triage agent with tool-calling access to policy administration, claims management, and third-party data systems has a broad action surface that extends well past its intended task. The governance question is not whether the model reaches a reasonable triage decision, but whether the agent's access to downstream systems is constrained to only the actions it was authorized to take.

This distinction matters because claims triage agents commonly require read access to coverage details, limits, and endorsements in policy administration systems, along with loss data and adjuster notes in claims management platforms. Some architectures also query third-party data sources such as property or vehicle databases. Each additional system connected to an agent increases the number of ways an ungoverned agent could exceed its intended scope, whether by modifying a record it should only read or invoking an action, such as payment initiation, that was never meant to be autonomous.

Defining AI Governance for Claims Triage

AI governance for claims triage refers to the combination of identity, permissioning, and runtime enforcement controls that constrain what an AI agent can access and do across policy and claims systems, paired with audit mechanisms that make every agent decision traceable and explainable after the fact. This is distinct from model governance, which focuses on training data, accuracy, and bias testing. Runtime governance addresses what happens when a deployed agent is live and calling tools against production systems.

NIST's AI Risk Management Framework frames this under its "Govern" function, which calls for defined roles and clear boundaries on what automated systems are authorized to execute versus merely recommend. That distinction between advisory output and autonomous action is the operational core of claims triage governance: an agent that recommends a severity tier is a fundamentally different risk than one that can independently reassign a claim or trigger a payment workflow.

Runtime Enforcement Architecture for Claims Triage Agents

A governed claims triage deployment places a policy enforcement point between the agent and every downstream system it touches, rather than relying solely on static role assignments made at deployment time. NIST's Zero Trust Architecture guidance (SP 800-207) describes this pattern generally: continuous verification and policy decision points that evaluate each action at the point of execution, not just at login or session start.

Tool-Call Governance and the Excessive Agency Problem

OWASP's guidance on large language model applications identifies excessive agency and insecure tool or plugin design as top risks for agents with system or API access. In a claims triage context, this risk manifests when an agent's tool-calling capability is not explicitly scoped: an agent built to classify and route claims may technically have the credentials or API access to invoke a payment endpoint or update policy status, even if that was never the intended use case.

Mitigating this requires explicit, enumerated permissions rather than implicit trust based on what the agent's underlying service account can reach. Every system an agent may touch (policy administration, claims management, third-party data, and payment) should be mapped and classified as read, write, or restricted before any access is granted. High-risk actions should be excluded from the agent's allow-list by default and require separate authorization to enable, with that authorization itself logged and reviewable.

Agent Identity: Treating Agents as Distinct Security Principals

Industry frameworks published in 2024 and 2025, including Microsoft's Entra Agent ID and Okta's guidance on AI agent identity, converge on a common principle: AI agents should be issued distinct identities rather than operating under the credentials of the human who deployed them or a shared service account. This allows access to be scoped, monitored, and revoked independently for each agent.

For claims triage specifically, this means a triage agent's permissions can be modified or revoked without affecting other agents or the underlying claims application. It also means that when an incident or anomaly occurs, forensic review can isolate exactly which agent, credential, and session was responsible, rather than tracing activity back to a shared account used by multiple systems or users. These are vendor-published approaches rather than ratified technical standards, but they reflect a consistent industry direction toward per-agent identity as a governance baseline.

Audit and Regulatory Expectations

The NAIC's Model Bulletin on the Use of Artificial Intelligence Systems by Insurers, adopted by multiple states in 2024, directs insurers to maintain written AI governance programs covering risk management, testing, and accountability for AI systems used in underwriting and claims. It further requires that insurers be able to explain and document decisions materially influenced by AI systems, including claims practices, upon request from state regulators.

This creates a practical requirement: audit logs must capture more than a triage agent's final output. They need to reconstruct the decision chain, including what data the agent accessed, which tool calls it made, what policy checks were applied, and what action resulted. NIST's AI RMF reinforces this with its recommendation for logging, traceability, and human review checkpoints on systems affecting consequential decisions. Insurers should also expect ongoing testing obligations under NAIC guidance, since pre-deployment and continuous testing for accuracy and unfair discrimination applies to any AI system influencing claims handling, not just underwriting.

Implementation Path for Governed Claims Triage Deployment

The considerations above translate into a common set of questions insurers raise when planning a governed rollout. These are addressed below.